Lights and Shadows in the Governance of Generative AI Firms: a preliminary approach

The emergence of Generative Artificial Intelligence (AI) has accelerated the growth of the AI sector, transforming business processes and redefining corporate decision-making. This paper examines the complex relationship between AI and governance through a multidimensional lens, emphasizing its non-linear nature. We identify three principal dimensions that form a triangular framework: Governance with AI, referring to the integration of AI into corporate decision-making; AI governance, encompassing regulatory frameworks that shape AI development; and Governance of AI firms, which examines internal governance models balancing innovation, profitability, and ethical concerns. Our analysis focuses specifically on the governance of AI firms, highlighting the factors that influence their structural choices. Through a qualitative investigation, this study identifies key governance models, including profit-distributing nonprofits (e.g., OpenAI), trust-controlled corporations (e.g., Anthropic), and open-source oriented organizations (e.g., Mistral). The findings suggest that governance structures are shaped by company size, geographic location, regulatory environment, and software distribution models (open-source vs. proprietary). This research offers valuable implications for AI stakeholders, suggesting that organizations should develop governance frameworks that balance innovation with ethical considerations while adapting to their specific market contexts. Larger companies, particularly those with proprietary models, should prioritize robust oversight mechanisms to build stakeholder trust. This study introduces an innovative tripartite analytical framework to examine the governance structures of generative artificial intelligence providers. This analysis, conducted in a research area that is still relatively under- investigated, aims to fill existing gaps in the scientific literature. Through this original approach, the study offers new perspectives on the governance dynamics that shape the generative AI industry, providing a significant contribution to the understanding of this rapidly evolving sector.